A Study on the Behaviour of the Algorithm for Finding Relevant Attributes and Membership Functions

Abstract

A Study on the Behaviour of the Algorithm for Finding Relevant Attributes and Membership Functions One of the most recent approaches in machine learning is fuzzy rules usage for solving classification problems. This paper describes the algorithm for finding relevant attributes and searching for membership functions. Experimental results are used to clarify - which data sets can be used to automatically gain primary membership functions from primary data. This quality - gaining of membership functions - is one of the pros of the algorithm, because it eases resolution of classification task. The ability to use it with fuzzy data is one more merit. As a result, there are obtained reliable fuzzy classification rules to separate classes. By reconstructing primary membership functions also the number of IF-THEN rules gained from decision tables is reduced up to three times. Four experiments are conducted with different training and testing data set sizes. Conclusions are made about the optimal size of the training and testing data set that is necessary for achieving better results as well as about the data this algorithm is appropriate for. Finally, possible directions for further research are outlined.